{"slug": "holographic-neural-pcfg-for-unsupervised-parsing", "title": "Holographic Neural PCFG for Unsupervised Parsing", "summary": "Researchers propose Holographic Neural PCFG (Hol-PCFG), a new unsupervised constituency parsing model that uses holographic embeddings to score grammar rules with a closed-form mathematical structure. Hol-PCFG achieves state-of-the-art parsing performance across six languages while reducing rule-scoring parameters by 99.94% compared to baseline models and enabling direct character-level parsing for Japanese.", "body_md": "arXiv:2607.08063v1 Announce Type: new\nAbstract: Unsupervised constituency parsing aims to accurately induce latent tree structures from raw text alone. Recent neural parameterizations of PCFGs achieve strong performance in both supervised and unsupervised parsing, yet rely on high-capacity black-box networks for rule scoring -- as exemplified by the Neural PCFG family -- leaving rule probabilities without an interpretable mathematical form. In this paper, we propose Holographic Neural PCFG (Hol-PCFG), which recasts PCFG rule scoring as algebraic relation modeling among grammar-symbol embeddings. Hol-PCFG adapts Holographic Embeddings (Nickel et al., 2016), which scores knowledge-graph triples via circular correlation, to the left-child, right-child, and lexical-emission relations over torus-constrained embeddings, giving every rule probability a closed form that carries the intrinsic structure of grammar rules by construction. Hol-PCFG achieves state-of-the-art parsing performance in six languages while cutting rule-scoring parameters by 99.94% relative to the baseline model and training more stably. Additionally, we demonstrate that Hol-PCFG can parse Japanese directly from characters without any morphological segmentation, retaining nearly the same morpheme-level performance.", "url": "https://wpnews.pro/news/holographic-neural-pcfg-for-unsupervised-parsing", "canonical_source": "https://arxiv.org/abs/2607.08063", "published_at": "2026-07-10 04:00:00+00:00", "updated_at": "2026-07-10 04:19:23.247664+00:00", "lang": "en", "topics": ["natural-language-processing", "machine-learning", "artificial-intelligence"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/holographic-neural-pcfg-for-unsupervised-parsing", "markdown": "https://wpnews.pro/news/holographic-neural-pcfg-for-unsupervised-parsing.md", "text": "https://wpnews.pro/news/holographic-neural-pcfg-for-unsupervised-parsing.txt", "jsonld": "https://wpnews.pro/news/holographic-neural-pcfg-for-unsupervised-parsing.jsonld"}}